CHAPTER 2: INTELLIGENT AGENTS An Introduction to Multiagent Systems - PowerPoint PPT Presentation

CHAPTER 2: INTELLIGENT AGENTS An Introduction to Multiagent Systems http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � Chapter 2 An Introduction to Multiagent Systems 1 What is an Agent? The main point about agents is they are autonomous : capable of acting independently, exhibiting control over their internal state. Thus: an agent is a computer system capable of autonomous action in some environment . SYSTEM input output ENVIRONMENT 1 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � Chapter 2 An Introduction to Multiagent Systems Trivial (non-interesting) agents: – thermostat; – UNIX daemon (e.g., biff). An intelligent agent is a computer system capable of flexible autonomous action in some environment. By flexible , we mean: – reactive ; – pro-active ; – social . 2 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � � � Chapter 2 An Introduction to Multiagent Systems 1.1 Reactivity If a program’s environment is guaranteed to be fixed, the program need never worry about its own success or failure — program just executes blindly. Example of fixed environment: compiler. The real world is not like that: things change, information is incomplete. Many (most?) interesting environments are dynamic . Software is hard to build for dynamic domains: program must take into account possibility of failure — ask itself whether it is worth executing! A reactive system is one that maintains an ongoing interaction with its environment, and responds to changes that occur in it (in time for the response to be useful). 3 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � � � � � Chapter 2 An Introduction to Multiagent Systems 1.2 Proactiveness Reacting to an environment is easy (e.g., stimulus response rules). But we generally want agents to do things for us . Hence goal directed behaviour . Pro-activeness = generating and attempting to achieve goals; not driven solely by events; taking the initiative. Recognising opportunities. 4 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � � � Chapter 2 An Introduction to Multiagent Systems 1.3 Social Ability The real world is a multi- agent environment: we cannot go around attempting to achieve goals without taking others into account. Some goals can only be achieved with the cooperation of others. Similarly for many computer environments: witness the INTERNET . Social ability in agents is the ability to interact with other agents (and possibly humans) via some kind of agent-communication language , and perhaps cooperate with others. 5 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � � � � Chapter 2 An Introduction to Multiagent Systems 2 Other Properties of Agency Sometimes Discussed Mobility : The ability of an agent to move around an electronic network. Veracity : Whether an agent will knowingly communicate false information. benevolence : Whether agents have conflicting goals, and thus whether they are inherently helpful. Rationality : Whether an agent will act in order to achieve its goals, and will not deliberately act so as to prevent its goals being achieved. Learning/adaption : Whether agents improve performance over time. 6 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � Chapter 2 An Introduction to Multiagent Systems 2.1 Agents and Objects Are agents just objects by another name? Object: – encapsulates some state; – communicates via message passing; – has methods, corresponding to operations that may be performed on this state. 7 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� Chapter 2 An Introduction to Multiagent Systems Main differences: – agents are autonomous : agents embody stronger notion of autonomy than objects, and in particular, they decide for themselves whether or not to perform an action on request from another agent; – agents are smart : capable of flexible (reactive, pro-active, social) behavior, and the standard object model has nothing to say about such types of behavior; – agents are active : a multi-agent system is inherently multi-threaded, in that each agent is assumed to have at least one thread of active control. 8 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � Chapter 2 An Introduction to Multiagent Systems Objects do it for free. . . agents do it because they want to; agents do it for money . 9 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � � Chapter 2 An Introduction to Multiagent Systems 2.2 Agents and Expert Systems Aren’t agents just expert systems by another name? Expert systems typically disembodied ‘expertise’ about some (abstract) domain of discourse. Example: MYCIN knows about blood diseases in humans. It has a wealth of knowledge about blood diseases, in the form of rules. A doctor can obtain expert advice about blood diseases by giving MYCIN facts, answering questions, and posing queries. 10 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � Chapter 2 An Introduction to Multiagent Systems Main differences: – agents situated in an environment : MYCIN is not aware of the world — only information obtained is by asking the user questions. – agents act : MYCIN does not operate on patients. Some real-time (typically process control) expert systems are agents. 11 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � � Chapter 2 An Introduction to Multiagent Systems 2.3 Intelligent Agents and AI Aren’t agents just the AI project? Isn’t building an agent what AI is all about? AI aims to build systems that can (ultimately) understand natural language, recognise and understand scenes, use common sense, think creatively, etc — all of which are very hard. So, don’t we need to solve all of AI to build an agent. . . ? 12 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � � Chapter 2 An Introduction to Multiagent Systems When building an agent, we simply want a system that can choose the right action to perform, typically in a limited domain. We do not have to solve all the problems of AI to build a useful agent: a little intelligence goes a long way! Oren Etzioni, speaking about the commercial experience of NETBOT , Inc: We made our agents dumber and dumber and dumber . . . until finally they made money. 13 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� Chapter 2 An Introduction to Multiagent Systems 3 Properties of Environments Accessible vs inaccessible . An accessible environment is one in which the agent can obtain complete, accurate, up-to-date information about the environment’s state. Most moderately complex environments (including, for example, the everyday physical world and the Internet) are inaccessible. The more accessible an environment is, the simpler it is to build agents to operate in it. 14 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� Chapter 2 An Introduction to Multiagent Systems Deterministic vs non-deterministic . As we have already mentioned, a deterministic environment is one in which any action has a single guaranteed effect — there is no uncertainty about the state that will result from performing an action. The physical world can to all intents and purposes be regarded as non-deterministic. Non-deterministic environments present greater problems for the agent designer. 15 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� Chapter 2 An Introduction to Multiagent Systems Episodic vs non-episodic . In an episodic environment, the performance of an agent is dependent on a number of discrete episodes, with no link between the performance of an agent in different scenarios. Episodic environments are simpler from the agent developer’s perspective because the agent can decide what action to perform based only on the current episode — it need not reason about the interactions between this and future episodes. 16 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� Chapter 2 An Introduction to Multiagent Systems Static vs dynamic . A static environment is one that can be assumed to remain unchanged except by the performance of actions by the agent. A dynamic environment is one that has other processes operating on it, and which hence changes in ways beyond the agent’s control. The physical world is a highly dynamic environment. 17 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� Chapter 2 An Introduction to Multiagent Systems Discrete vs continuous . An environment is discrete if there are a fixed, finite number of actions and percepts in it. Russell and Norvig give a chess game as an example of a discrete environment, and taxi driving as an example of a continuous one. 18 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/

� � � Chapter 2 An Introduction to Multiagent Systems 4 Agents as Intentional Systems When explaining human activity, it is often useful to make statements such as the following: Janine took her umbrella because she believed it was going to rain. Michael worked hard because he wanted to possess a PhD. These statements make use of a folk psychology , by which human behaviour is predicted and explained through the attribution of attitudes , such as believing and wanting (as in the above examples), hoping, fearing, and so on. The attitudes employed in such folk psychological descriptions are called the intentional notions. 19 http://www.csc.liv.ac.uk/˜mjw/pubs/imas/